The Okapi project’s main purpose is to architect a set of building blocks for the creation of larger open source localization and translation tools. But many Okapi components are generic enough to be of interest to the text mining, natural language processing, and text retrieval communities. Okapi’s many text filters (HTML, Properties, XML (ITS XPath-based rules), OpenXML, ODF, Regex etc.) provide a straightforward way to access the text of multiple document formats. Its document events and pipeline can be made to integrate with other frameworks such as UIMA, LingPipe, OpenPipeline, OpenNLP, GATE, and Lucene. The advantage of Okapi’s text filters is that not only is text extracted, but all non-textual formatting is preserved. It is possible to decompose a document into events, process them via the pipeline, and then rebuild the input document without loss. Structural information can be added to Okapi document events so that tables, lists, links, titles etc. are grouped together and treated as a unit. This is useful when context based on a “universal” document structure is needed. The Okapi event model supports user configurable annotations, similar to UIMA, but simpler and more restricted in scope. User can annotate spans of text or add new resources such as translation memory matches, terminology, token types, or part of speech information.

ACOPOST is a set of freely available POS taggers modeled after well-known techniques. The programs are written in C (aiming for extreme portability and code correctness/safety) and run under various Unix flavors (and probably even under Windows). ACOPOST currently consists of four taggers that are based on different frameworks: Maximum Entropy Tagger (MET), Trigram Tagger (T3, based on Hidden Markov Models), Error-driven Transformation-based Tagger (TBT or Brill Tagger), and Example-based tagger (ET).

The AlchemyAPI Android SDK enables real-time semantic analysis of text, HTML, or Internet-hosted Web page content. The SDK provides mechanisms to extract Concepts, Named Entities, Keywords and Tags, Categories, and clean HTML into text, and even detects languages. It can analyze text in eight different languages: English, French, German, Italian, Portuguese, Russian, Spanish, and Swedish. Example code and a demo application are included to help get you started.

Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services.

foma is a compiler, programming language, and C library for constructing finite-state automata and transducers for various uses. It has specific support for many natural language processing applications such as producing morphological analyzers. Although NLP applications are probably the main use of foma, it is sufficiently generic to use for a large number of purposes. It comes with an xfst-compatible interface and regular expression language. The library contains efficient implementations of all classical automata/transducer algorithms: determinization, minimization, epsilon-removal, composition, and boolean operations. More advanced construction methods are also available: context restriction, quotients, first-order regular logic, transducers from replacement rules, etc.

TreeTagger for Java (TT4J) is a Java wrapper around the popular TreeTagger package by Helmut Schmid, a language independent part-of-speech tagger and lemmatizer. It was written with a focus on platform-independence and easy integration into applications.

jWeb1T is an Java tool for efficiently searching n-gram data in the Web 1T 5-gram corpus format. It is based on a binary search algorithm that finds the n-grams and returns their frequency counts in logarithmic time. As the corpus is stored in many files, a simple index is used to retrieve the files containing the n-grams.